Evidence map›Paper›PMID 37907600›Full record

ArticleHypertension research : official journal of the Japanese Society of Hypertension2024

Adopting artificial intelligence in cardiovascular medicine: a scoping review.

Hisaki Makimoto, Takahide Kohro

Abstract readScoping Review
PubMed Publisher
In one paragraph

Article in Hypertension research : official journal of the Japanese Society of Hypertension, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Review
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  3. A multi-layer retrieval-augmented large language model framework for enhancing hypertension education.Hypertension research : official journal of the Japanese Society of Hypertension · 2026
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  4. Review
  5. Article
  6. Digital ECG detection of flat T waves predicts cardiovascular events in high-risk patients: the Coupling Study.Hypertension research : official journal of the Japanese Society of Hypertension · 2026
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Hisaki MakimotoData Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan. h1sak1mak1m0t0@gmail.com.
Takahide KohroData Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.

Indexed as

CardiologyCardiovascular AgentsHeart FailureArtificial IntelligenceElectrocardiographyHumansCardiovascular AgentsArtificial intelligenceCardiovascular medicineDeep learningMachine learning

Identifiers

PMID37907600

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.